Model Explainer

Feature Importances

Model performance metrics

metric Score
accuracy 0.883
precision 1.0
recall 0.042
f1 0.082
roc_auc_score 0.995
pr_auc_score 0.946
log_loss 0.197

Confusion Matrix

How many false positives and false negatives?

Precision Plot

Does fraction positive increase with predicted probability?

Classification Plot

Distribution of labels above and below cutoff

ROC AUC Plot

Trade-off between False positives and false negatives

PR AUC Plot

Trade-off between Precision and Recall

Lift Curve

Performance how much better than random?

Cumulative Precision

Expected distribution for highest scores

Individual Predictions

Select Random Index

Selected index: None

Prediction

no index selected

Contributions Plot

How has each feature contributed to the prediction?
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Partial Dependence Plot

Contributions Table

How has each feature contributed to the prediction?
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What if...

Select Random Index

Selected index: None

Prediction

input data incorrect

Feature Input

Adjust the feature values to change the prediction
Selected: None

Contributions Plot

How has each feature contributed to the prediction?
input data incorrect

Partial Dependence Plot

input data incorrect

Contributions Table

How has each feature contributed to the prediction?
input data incorrect

Feature Dependence

Shap Summary

Ordering features by shap value

Shap Dependence

Relationship between feature value and SHAP value

Feature Interactions

Interactions Summary

Ordering features by shap interaction value

Interaction Dependence

Relation between feature value and shap interaction value

Decision Trees

Select Random Index

Selected index: None

Decision Trees

Displaying individual decision trees inside Random Forest
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Decision path table

Decision path through decision tree
no tree selected